Extracting characteristics of traffic flow in bottlenecks with exchange interactions in time headway

Sag sections (sections that change grade) have recently been recognized as possible capacity bottlenecks. This study examines factors in capacity bottlenecks. First, it is shown that the relation between longitudinal alignment and traffic flow is important, whereas differences in driver characteristics, number of lanes, and overtaking have secondary effects since the bottleneck position is independent of day, time slot, and number of lanes. Second, to clarify the effect of longitudinal alignment on traffic flow, the accumulation of car-following method on a driving simulator is applied. Third, because traditional approaches that model traffic flow by car-following behavioral models and traffic flow simulation do not necessarily succeed in predicting bottleneck position, a new idea is proposed to extract the influence of longitudinal alignment on traffic flow. Based on the existence of exchange interactions in headways, the characteristics of traffic flow are condensed to a pair of variables, called hyperparameters, by hierarchical Bayesian estimation. The estimated hyperparameters are used to estimate flow. Specifically, a proxy traffic flow is generated, and finally the applicability of the idea of model exchange interactions in time headway is discussed.